Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.

It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.

Commentary: Dimensionality in environmental adversity, mechanisms of emotional socialization, and children's characteristics and cognitive growth - a reflection on Miller et al. (2020).

Disentangling the dimensionality in environmental adversity offers nuanced insights at both theoretical and practical levels, such as the ways that disadvantaged socioeconomic circumstances during childhood development may contribute to adolescent psychopathology. Miller and colleagues (2020) provide evidence into how early deprivation and threat may exacerbate later psychopathology. Yet, how certain factors in this early environment differentially facilitate children's cognitive and socioemotional growth may modulate the severity of later psychopathology. In this commentary, we reflect on the promising evidence offered by Miller and colleagues and extend additional considerations regarding academic growth, cognitive abilities, and protective environmental factors.

Readers Recruit Executive Functions to Self-Correct Miscues During Oral Reading Fluency.

Reading fluency undoubtedly underlies reading competence; yet, the role of executive functions (EF) is less well understood. Here, we investigated the relationship between children's reading fluency and EF. Children's (n = 82) reading and language performance was determined by standardized assessments and EF by parental questionnaire. Results revealed that production of more miscues was explained by poorer reading and language performance and EF. Yet, self-correcting a miscue was predicted by better EF, beyond reading and language abilities. Intriguingly, EF partially mediated the relationship between reading and self-correction, suggesting that self-correction reflects parallel recruitment and coordination of domain-specific and domain-general processes.

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes.

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, "total UBO volume", defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.

Objective: To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). Materials and Methods: Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface. Results: A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities. Discussion: pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories. Conclusion: pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection.

pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.

Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .

Tracking Familial History of Reading and Math Difficulties in Children's Academic Outcomes.

The current study aimed to investigate the extent to which familial history of reading and math difficulties have an impact on children's academic outcomes within a 3-year longitudinal study, which evaluated their core reading and math skills after first (N = 198; 53% girls) and second grades (N = 166), as well as performance on complex academic tasks after second and third grades (N = 148). At baseline, parents were asked to complete the Adult Reading History Questionnaire (ARHQ) and its adaption, Adult Math History Questionnaire (AMHQ), to index familial history of reading and math difficulties, respectively. Preliminary findings established the psychometric properties of the AMHQ, suggesting that it is a reliable and valid scale. Correlation analyses indicated that the ARHQ was negatively associated with children's reading skills, whereas the AMHQ was negatively related to math outcomes. Path results revealed that the ARHQ predicted children's performance on complex reading tasks indirectly via their core reading skills, and the AMHQ was linked to complex math outcomes indirectly via core math abilities. The ARHQ was also found to be negatively correlated with measures of children's math performance, with path findings suggesting that these relations were indirectly explained by differences in their core reading skills. These results suggest that assessing familial risk for academic difficulties may be crucial to understanding comorbid etiological and developmental associations between reading and math differences.

Executive functions and components of oral reading fluency through the lens of text complexity.

As readers struggle to coordinate various reading- and language-related skills during oral reading fluency (ORF), miscues can emerge, especially when processing complex texts. Following a miscue, students often self-correct as a strategy to potentially restore ORF and online linguistic comprehension. Executive functions (EF) are hypothesized to play an interactive role during ORF. Yet, the role of EF in self-corrections while reading complex texts remains elusive. To this end, we evaluated the relation between students' probability of self-correcting miscues-or P(SC)-and their EF profile in a cohort of 143 participants (aged 9-15) who represented a diverse spectrum of reading abilities. Moreover, we used experimentally-manipulated passages (decoding, vocabulary, syntax, and cohesion) and employed a fully cross-classified mixed-effects multilevel regression strategy to evaluate the interplay between components of ORF, EF, and text complexity. Our results revealed that, after controlling for reading and language abilities, increased production of miscues across different passage conditions was explained by worse EF. We also found that students with better EF exhibited greater P(SC) when reading complex texts. While text complexity taxes students' EF and influences their production of miscues, findings suggest that EF may be interactively recruited to restore ORF via self-correcting oral reading errors. Overall, our results suggest that domain-general processes (e.g., EF) are associated with production of miscues and may underlie students' behavior of self-corrections, especially when reading complex texts. Further understanding of the relation between different components of ORF and cognitive processes may inform intervention strategies to improve reading proficiency and overall academic performance.

Applying a network framework to the neurobiology of reading and dyslexia.

BACKGROUND: There is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading. METHODS: First, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain's tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions. RESULTS: We found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual's brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas. CONCLUSIONS: These results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

Endoscopic ultrasound guided fine needle aspiration (EUS-FNA) of mediastinal lesions.

BACKGROUND: Mediastinal endoscopic ultrasound guided fine needle aspiration (EUS-FNA) is a recognized diagnostic and staging procedure for non-small cell lung carcinoma (NSCLC). The aim of this study was to report the experience of mediastinal EUS in an Australian tertiary hospital. METHODS: A retrospective review was conducted on all patients undergoing mediastinal EUS from February 2002 until August 2007 at St Vincent's Hospital, Melbourne. Data were obtained from the EUS databases at St Vincent's Hospital and patient endoscopy reports. The results of EUS-FNA were compared with final diagnosis to calculate sensitivity and specificity. Surgical pathology or long-term follow-up was used to identify false positive or negative results. RESULTS: One hundred forty-eight mediastinal EUS procedure were performed. Males comprised 63.5% and the mean age was 64.3 (range 27-85). Referrals (47%) were from respiratory physicians and 27% were from cardiothoracic surgeons. Indications for EUS-FNA included unexplained mediastinal lymphadenopathy and/or lung lesion for investigation and staging of known NSCLC. Full data were available on 124 (83.8%) cases. Data were analysed from a subset of 112 where FNA was performed. For each indication, EUS-FNA had a high sensitivity and specificity: staging of known NSCLC (sensitivity 92.9%, specificity 88.9%), mediastinal lymphadenopathy (sensitivity 100%, specificity 100%) and lung lesion (sensitivity 94.4%, specificity 85.7%). There were no major complications. CONCLUSION: This large series of mediastinal EUS shows that it is an important and useful tool for the assessment of mediastinal pathology. It is safe and highly accurate, and should be incorporated into the staging algorithm for NSCLC.